Application of a multi-layer feedforward neural network to predict fracture density in shale oil, Junggar Basin, China

Abstract

Fengcheng Formation in the Mabei Slope of Junggar Basin has low porosity and permeability. However, fractures are well developed, representing an effective storage space for shale oil. Core and experimental data show that the shale oil reservoir of Fengcheng Formation positively correlates with oil content and fractures. And the fracture density has a good quantitatively positive correlation with crude oil production from the production data. Fengcheng Formation has been significantly enriched and accumulated with shale oil due to fractures serving as reservoirs and seepage channels. Therefore, quantitative prediction of fractures is the key to finding high production areas of shale oil in the Fengcheng Formation. The purpose of this study is to extract the seismic attributes that are sensitive to shale oil reservoir fractures. These attributes include curvature, deep learning fracture detection, maximum likelihood, eigenvalue coherence, and variance cube. Furthermore, a seismic multi-attribute fracture density prediction model is trained at the well point using a feedforward neural network method, and the spatial distribution of fracture density is predicted. The results show that the predicted fracture density is consistent with the formation micro imaging logs in the area. Simultaneously, combined with the understanding of the quantitative relationship between fracture density and shale oil production, quantitative prediction results of fracture density could provide the basis for determining the distribution and optimal location of high-quality shale oil wells in the study area. This study will serve as a benchmark for identifying fractures in shale oil reservoirs worldwide.